Andic soils and catastrophic mudflows in Italy: morphological and hydropedological evidences

In Italy rapid landslides are the most frequently occurring natural disasters and, after earthquakes, cause the highest number of victims. In this contribution we attempt to prove that there exist a tight connection between the presence of a specific soil type, namely andic soils, and the occurrence of the main catastrophic mudflows and debris flows occurred in Italy in the last decades. The study was performed by means of an integrated pedological and hydrological analysis on the detachment crowns of some of the most important catastrophic mudflows and debris flows occurred in Italy in the last decades and involving/evolving surface soils. The results at both regional (Campania) and National (Italy) scale clearly show that despite the large variability of the environmental settings of the studied sites there are indeed some striking homogeneous soil features in the detachment crowns including (i) soil morphology, (ii) andic features ranging from high to moderate, (iii) high water retention throughout a large range of pressure heads. Results seem to reveal clear cause-effect evidences between andic soils and the investigated catastrophic mudflows/debrisflows; this must be related to the unique physical properties of these soils inducing high landslide vulnerability

Soils of the Aversa plain (southern Italy)

The Aversa plain is one of the most important agricultural areas of the Campania region, combining the presence of very fertile soils, sites of great archaeological interest and growing residential urbanization. In this paper, the soil map (1:50,000 scale) of the Aversa plain is presented. Three main land systems (coastal, alluvial and foothill plains) characterized by different soil types (Andosols, Phaeozems, Cambisols, Vertisols, Arenosols, Histosols, Luvisols) have been identified. However, Andosols are the most widespread soil type (9768 ha) and, along with part of the Phaeozems and Cambisols, represent the most fertile soils of the Aversa plain (first and second classes of the land capability classification). In order to evaluate recent intense soil sealing, its impact over land capability classes was assessed during the last 60 years. Results show that soil sealing in the Aversa plain affected mainly the most fertile first- and second-class soils

Monitoraggio dell'impatto del consumo di suolo sulle infrastrutture verdi dell'area metropolitana di Napoli attraverso Soil Monitor

L’Osservatorio sul Consumo di Suolo dell’INU Campania ha testato il 10 e il 31 maggio presso, Laboratorio informatico del Centro LUPT - Università Federico II il tool “Soil Monitor” come prodotto di una ricerca Life messo a punto dal CRISP - Centro di ricerca interdipartimentale sulla “Earth Critical Zone” per il supporto alla gestione del paesaggio e dell’agroambiente. Quindici soci INU hanno partecipato al test che ha previsto un'esercitazione guidata su tre sessioni di esercizi relativi all’utilizzo di indicatori sul consumo di suolo, sulla frammentazione ecologica e sulle gerarchie urbane. Al fine di perfezionare l’applicazione e renderla più accessibile a potenziali utenti, si è svolta una successiva sperimentazione su un’area territoriale mirata ma significativa coincidente con l’area metropolitana di Napoli. I risultati ricavati dall’utilizzo degli indicatori del “Soil Monitor” sono stati messi a confronto con le analisi ambientali e la proposta di Rete Ecologica Principale del Piano Territoriale di Coordinamento dell’area metropolitana di Napoli (PTC), adottato nel gennaio 2016 e sospeso il successivo aprile. Gli esiti del test sono presentati in quest’articolo

ReFuse: Generating Imperviousness Maps from Multi-Spectral Sentinel-2 Satellite Imagery

Continual mapping and monitoring of impervious surfaces are crucial activities to support sustainable urban management strategies and to plan effective actions for environmental changes. In this context, impervious surface coverage is increasingly becoming an essential indicator for assessing urbanization and environmental quality, with several works relying on satellite imagery to determine it. However, although satellite imagery is typically available with a frequency of 3–10 days worldwide, imperviousness maps are released at most annually as they require a huge human effort to be produced and validated. Attempts have been made to extract imperviousness maps from satellite images using machine learning, but (i) the scarcity of reliable and detailed ground truth (ii) together with the need to manage different spectral bands (iii) while making the resulting system easily accessible to the end users is limiting their diffusion. To tackle these problems, in this work we introduce a deep-learning-based approach to extract imperviousness maps from multi-spectral Sentinel-2 images leveraging a very detailed imperviousness map realised by the Italian department for environment protection as ground truth. We also propose a scalable and portable inference pipeline designed to easily scale the approach, integrating it into a web-based Geographic Information System (GIS) application. As a result, even non-expert GIS users can quickly and easily calculate impervious surfaces for any place on Earth (accuracy >95%), with a frequency limited only by the availability of new satellite images